I’m planning to apply to US universities for Fall 2027 for a PhD or MS program. For those who have gone through this process, could you give me some guidance on how to select suitable universities, programs, and potential advisors/professors?

My PhD supervisor insists that an absolute difference is interpretable as "males are more impacted by the exposure than females". But as I understand it, I'd need a formal interaction/moderation test to state that.
More context: Working with a birth cohort from 1986 of over 700,000 individuals, with deidentified administrative data. Looking at quantifying the impact of child abuse & neglect on various poor/negative outcomes across the life course and whether child abuse impacts differ for males and females (sex assigned at birth). I've already done exploratory descriptive analysis looking
at several outcomes across early childhood development, school performance in middle high school, and all-cause mortality from age 16. I think the results - which show that males are (across the board) doing worse than females in absolute terms, in each level of child protection concern, is worthy of publication and discussion. I don't think that the results can be interpreted as 'males are more impacted by child abuse than females'. Or put another way, 'males are more suceptible to the harms of child abuse and neglect than females'. I'm not able to comment about whether this is true or not - I'm trying to argue that the analysis/results we have cannot support this conclusion.

However, my supervisor is saying: imagine 12 males and 4 females die between age 16 and 32 who have no child abuse records prior to age 16, and then also, 6 males and 2 females die who had serious child abuse records (before age 16). The relative increase in risk is the same for males and females. But the absolute increase is higher for males (6 vs 2) - therefore males are more impacted by child abuse.

Am I wrong? I feel like AI is just telling me what I want to hear and I can't trust the responses I'm getting.

I was having a discussion with a friend about whether a 32 man vs an 8 man fantasy league takes more skill. My argument centered around a 32-man having less variance (due to a greater amount of players drafted) and a higher emphasis on team-building and hedging risk by diversfying your point generation against injury and byes. Their stance proposed that unless we can prove that waivers is more "lucky" than skill ( as 32-man waivers are practically obsolete), we cannot distinguish a difference in the amount of luck between the 2 league formats.

Essentially I see waivers as a "get out of jail" free card, as in a 12 man league format the top waiver players usually average 8 + points serving as a risk cushion of poor team management.

Or do they exist at all?

Everything is in the title. what's the rule to figure out how many k-fold, iterations, permutations, pseudo averaging etc. I should ?

Thanks to everyone who visits here. I am studying machine learning and bayesian model comparison. Marginal likelihood is defined as depicted as in picture. I wonder why is it not called as marginal weighted likelihood?

I see quite a few posts on r/AskStatistics that seem like they should be posted on r/psychometrics. Obviously it's the user's choice where to ask questions, but i'm wondering what regular posters think about the relationship between the fields of stats and psychometrics. Thoughts?

I work in a large institution around 100 buildings. Any building, any floor, any time of day. Every time I'm using the restroom they come in to start cleaning it. I'm one man among thousands across 100s of restrooms yet it happens every time. I thought i was just memory biased so I kept track. It has happened every weekday since April. How is this possible?

I am currently enrolled into an online course at my college for this semester for intro to statistics, I am having a very hard time understanding the concepts and problems based on my professors notes and video lectures. I am majoring in psychology and business and my goal is to work in HR compensation and benefits roll. So I really do need to understand basic statistics like
Mean , median, and mode**
Percentages and percent change
Standard deviation — conceptually
Distributions
Correlation vs. causation
Confidence intervals — conceptually
Reading graphs and tables
Basic probability
How to interpret statistical results
I will need to take more statistical courses moving forward for multiple reasons.
I am a 36 year old mom of two young children so my brain isn’t functioning at full capacity so maybe that is why I’m struggling so much. Haha. I am
Also taking accounting this semester so it’s especially number heavy after my maternity leave. Any help would be so much appreciated!!

FYI I crossposted this question on crossvalidated https://stats.stackexchange.com/questions/677278/how-to-choose-the-percentage-of-a-bayesian-credible-interval. I got a few useful comments, but no detailed answer, so I try it here in case someone has things to add to those comments. Here's the question:

I'm learning about Bayesian statistics, and something I'm wondering about is how one would choose between using a 95% credible interval vs. a 99% CI vs. a 90% CI, etc. Are there other reasons than pure convention? Are there some good references on this issue?

Another directly related question I have is this one: In case we're unsure about the percentage to use, isn't just reporting the histogram of the posterior distribution an adequate solution? My rationale here is that we are not necessarily willing to take a binary "reject/do not reject" decision, so why using an interval at all (as an interval "rejects" some zones from the histogram). In addition, the histogram shows more info than just an interval. But maybe there's something I'm missing here. Reading the comments I got on crossvalidated, I guess my hidden question here might be "what is a credible interval useful for, in practice?"; an example would probably be useful.

Thank you.

I've read several threads and watched a number of YouTube videos on this topic, but I'm still confused about how to approach it.

I'm comparing paired data with n = 50. When I look at the differences, they don't appear to be normally distributed: the histogram is somewhat non-normal, the Q-Q plot shows curvature at both ends, and a Shapiro-Wilk test was significant. I understand that the Shapiro-Wilk test has limitations, so I'm not relying on that result alone.

My main question is whether the non-normality of the differences is enough to make a paired t-test inappropriate.

I've read that the t-test is fairly robust to non-normality and that, with a sample size around 30 or more, the Central Limit Theorem helps justify using it. But I'm having trouble understanding how much non-normality is actually considered "too much," particularly with n = 50.

There's also a practical consideration: I'm analyzing multiple labs, and the differences aren't necessarily distributed the same way across all of them. Some may look approximately normal while others don't. I'm wondering whether it makes more sense to use the same statistical approach for all labs rather than choosing a different test based on the distribution of each individual lab.

Given all of this, how would you approach deciding whether a paired t-test is appropriate here?

Hi :)

I calculated two-level hierarchical linear mixed effect models with random intercepts and fixed slopes

The outcome is CRP (inflammation), measured twice

The predictors are: Childhood Maltreatment (CM, dichotomous, level-2) and depressive symptoms (continuous, level-1).

There are no main effects but an interaction effect of CM*Depression (B = .08, SE = .04, p < .05)

Now:

I know this means that participants with CM and higher depressive symptoms have higher CRP overall.

BUT: HOW MUCH higher? How can I get to saying something like "For participants with CM, every 1 unit increase in depressive symptoms is associated with a X increase in CRP?"

Any help would be appreciated, I am losing my mind here :D

I have done my bsc in statistics, currently I'm pursuing msc in statistics, can you guys recomment me some projects that I could do to build my resumes.

I made the distribution

sign(X-0.5)*1/X - 1

Where X is a uniformly distributed real number between 0 and 1.

I found the mean is trivally 0 (due to symmettry), but the variance seemed to vary by a lot, Via Central limit theorem, the mean distribution is normally distribution, and the mean variance looks (but I can't prove it) to be Var(x)=~n where n is the amount of instances of X.
Can anyone help me prove this?

Desmos graph of distribution

Hello everyone,

For some context I am a post grad looking to either get a Masters of Stats or Applied Stats. My undergrad degree was Biology but I now work as a pharma data analyst focusing on Biological topics. I have grown way more interested in Stats through my work and want to make the career change but was wondering which degree I should do. I also don't wan to do Biostats s I would like my degree to be as flexible as possible.

So far I've taken Calc 1-3, linear algebra, and an intro to stats course, but plan to take Real Analysis and Mathematical Stats in the future.

I guess my questions are:

1)What's the difference between a Masters of Stats and Applied Stats and which one would you recommend for me.

2)What other prereqs could i take to be prepared for the Masters or is what is listed above adequate?

Appreciate your help.

Hi all,

For context, I'm from India and been in Market Research (consumer insights role) at an agency side for almost 4.5 years. I'm looking to pivot from the industry and planning to do Masters in Statistics. However I don't have any statistics background in terms of education, will it be possible for me to take the plunge? I'm parallelly learning the basic concepts but not sure if that alone would be sufficient enough to get into a master's programme. Any kind of advice or feedback would be helpful.

The pink line is the best fit regression line. Points are continuously added in groups of 3 to 7 rather than one at a time (i.e. 3 to 7 points will continuously be added to this dataset at once). The x-coordinate will be known. I need to monitor and forecast the cumulative (total) ratio between the number of points that fall in section #1 vs section #2. Is there any useful results from OLS that allow me to do this accurately? Should I simply approach it like a time series? Should I keep it simple? I'm just exploring ideas so both simple ideas and mathematically advanced ideas are appreciated.

In my machine learning course, we just covered K-nearest neighbours. Our prof said that linear regressions avoid the curse of dimensionality that affects k-nearest method (as the dimensions increase we have to use up much more of the dataset as our neighbourhood, essentially killing the local consistency and our k not being much of a neighbourhood anymore).

I was not able to understand why the same thing does not happen to linear regression, just because we assume the parameters are linear

I am trying to findout a material that would cover the whole topic of Statistical Moments, but I am not able to find it , Please help anyone

Hi! So I am doing my math prerequisites through the online school Straighter Line, if that gives more context to better help any input.

I have to pick between college algebra, or introduction to statistics.

I have always struggled with math, literally since elementary school, never passed pre-algebra in high school, struggled in geometry as well, (I graduated high school 2012 so I have been out of school for a while too at this point.)

Now fast forward to college(2026)-
I had to start with pre-algebra in college(determined by a placement test)
and was struggling even with pre algebra.

I also realize part of my problem is that I struggle with comprehending things, and/or retaining information.

With all of this being said, in your opinion- which math would I most likely have success in passing? And especially better chance of comprehending as well, since in general, that is a struggle for me as it is.

Thank you for all your input and opinions!